A computational model of symbiotic composition in evolutionary transitions

Area of Science:

• Evolutionary biology
• Systems biology
• Theoretical biology

Background:

• Major evolutionary transitions often involve the integration of existing entities into novel, higher-level organizations.
• This compositional process, distinct from gradual mutation, offers a unique mechanism for generating variation and increasing evolvability.
• Examples include the symbiogenic origin of eukaryotes from prokaryotes.

Purpose of the Study:

• To explore and develop concepts of symbiotic composition as a driver of evolutionary innovation.
• To examine the impact of symbiotic composition on evolvability using an abstract model.
• To investigate how composition facilitates adaptation in complex, rugged fitness landscapes.

Main Methods:

• Development of a simple abstract model for symbiotic composition.
• Utilizing a scale-invariant fitness landscape with multi-scale ruggedness.
• Simulating evolutionary processes under mutation, conventional algorithms, and compositional approaches.

Main Results:

• Symbiotic composition provides a distinct pathway for evolutionary innovation, differing from mutation and conventional algorithms.
• Composition allows for a 'divide and conquer' strategy, decomposing the adaptive domain.
• This hierarchical composition mechanism scales effectively, enabling adaptation in challenging, rugged fitness landscapes where other methods falter.

Conclusions:

• Symbiotic composition is a powerful evolutionary mechanism that enhances evolvability.
• It offers a robust strategy for navigating complex adaptive landscapes by assembling pre-adapted components.
• This model highlights the importance of higher-level organization in driving evolutionary innovation and complexity.